Ep 67: Max Junestrand (CEO, Legora) on Differentiating and Pricing AI Apps & How the Legal Industry Will Evolve
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Today's Unsupervised Learning was a particularly fun one. We had on one of my portfolio company CEOs, Max Unistron, who is the CEO and co-founder of Legora. Legora is at the forefront of applying AI to the law industry. They're working with many of the top law firms around the world. They've raised over $100 million and honestly, one of the fastest growing AI applications out there. I was joined by my colleague, Logan Bartlett, and the three of us hit on all sorts of things, including Max's take on the future of the law space, how he thinks about product prioritization given the rapidly improving models,

and what he thinks about building versus leading to the models. And we also talked about what it's like expanding across a bunch of different global markets. This was an awesome conversation. Without further ado, here's Max.

Max, thanks so much for coming on the pod. Yeah, thanks for having me. We've been excited to do this one for a while. So have I. It's great to be here from Stockholm. Yeah, we've got all our Redpoint podcast hosts today. Yeah, exactly. This is my first appearance on Unsupervised Learning. Yeah, we've tried to keep you out for a long time, but you're destined to wear a staff. You don't want to water down the quality that is this podcast.

Yeah, exactly. I appreciate you having me on. We'll try and do like an NIL crossover episode. Yeah, yeah. You're keeping me in my little box on my podcast. Well, Max, a ton of stuff I know our listeners will be eager to hear about. Maybe to start, can you just contextualize for us? Like, where are we in like the AI meets law space today? Like what works, what doesn't? Right. Well, when we started, nothing was working. We were using the early- What was that? The early BERT models from Google. And I mean, they were decent in English and

horrendously bad in Swedish. And this was back in 2020. So when GPT arrived, 3.5, that was like the paradigm starter, if you will. Since then, I think we've moved from full on experimentation and trying to get stuff to work into actually implementing things that are

Well, really taking end-to-end work deliverables. Just to give one example, if you're doing a due diligence today, you're not going physically into the data room, you're not using Control-F, you're just taking all the documents, putting them in Legora, saying what you want to find, and then it finds it. And then based on those findings, we generate the report. And so things are really starting to move from,

empty queries against like a data set to okay this is the process that we want the llm to follow and what we do is we give an agent access to tools it then plans executes on that plan using the tools and then we get an end-to-end work deliverable that's actually usable

And where do you think this is all going? Obviously, these models get better every like three, six months. If we were to ask you to have a crystal ball and say where the legal field's going in five, ten years, what do you see ahead? So the interesting thing with the models getting better, that's one piece. But what we're getting the most leverage out of is actually all these surrounding frameworks like function calling, tool calling, MCP, and...

The legal software space has been incredibly fragmented. That was one of the early things I saw coming from outside the industry. You had one tool for translations, one tool for document comparisons, another tool for searching, and another one for reviewing. And now, suddenly, all of this is kind of getting baked together.

And you can, of course, also imagine a scale of sort of the complexity of legal work. And at the bottom, you have something very simple, like just finding data extraction. And at the top, you have something really complex, like drafting a share purchase agreement. And we've already started to

fully automate a lot of the bottom quartile in this graph, right? And we're sort of slowly but surely moving up. And I think here, the really interesting thing is for law firms and legal professionals to see where are they going to add the most leverage? Where do we need their specific expertise, their context, their way of instructing the models, and where are the sort of off-the-shelf LLMs just plainly good enough as they are?

I'm sure we have a lot of people listening that are aware of the legal field and the implications for it. But can you maybe speak to some of the elements of law and why it's uniquely well-suited for AI as a potential application? Yeah, sure. So I don't come from the law space. I was an engineer before.

When we first arrived, I think the first thing that you notice is there hasn't really been that much software developed in this space. There's industry-specific incentives that perhaps doesn't always align and incentivizes being more efficient and using software. So basically the hottest thing or the coolest thing that you could do was use a templating system. And what you basically have is this dilemma of law firms,

and in-house councils, and they do very different things.

In-house counsels primarily work with the same stuff over and over again. It might be NDA reviews, MSAs. You're controlling the risk for the business. When you work with a law firm, they typically do more one-offs or complex projects or things where there might not be as much precedent and so on. I think very broadly, you can sort of stitch it up in reviewing, reading, drafting, writing, or researching.

In the beginning, I think every piece of software in this industry was very focused on one of these. But because AI is kind of capable of doing things across this entire stack, platforms like Legora have emerged that are not solving a point solution, but rather serving this entire wall-to-wall of needs. And what we've really found is law firms want to lead in this new world.

paradigm, this new future. And for law firms who are not really leaning in, I think you kind of risk two things. One is not upskilling your team in this new future and this new paradigm in terms of how you're supposed to work. But increasingly so, clients are putting pressure on law firms to make this shift because they're starting to use tools like this internally. A lot of CEOs you see on Twitter and LinkedIn posting about, we're going to go AI first, right? And you can't motivate...

You can't motivate getting a new headcount unless you can prove to us that you've been more efficient. I think it was actually the Fiverr CEO who also mentioned Legora for their legal stack. And I think that is, to me, the most interesting thing when you start to blur the edge of what is software and what is service. Because really interestingly in the legal business, software is like a $20 billion market and legal services is like a trillion dollar market. Yeah.

Yeah, it's interesting. I feel like so many people thought this hourly billing problem would make it really hard to adopt software. But I think you're totally right that if it's something that your clients are using, you need to be on top of it as well. Yeah. And maybe clarify that point. The hourly billing problem is sort of an incentive-based. Yeah, that basically, if the pricing model is I bill for every hour I do, if you make me 50% more efficient, I'm billing for 50% less hours. But it's like,

you know, I don't know. I feel like lawyers used to what they used to go manually look up things in libraries and now they have databases and there's definitely been examples of things that have been adopted. For sure. And one thing that maybe it's not as clear from outside of the industry is that there's a ton of write offs and price pressure. A due diligence used to be really expensive and now it's

almost starting to get to the point where clients aren't even willing to pay for it. If you're a large PE client of a law firm, you will for sure pay for the advisory to the board when you're buying a company, but you might not pay for the contract review. And already you see the large American firms outsourcing this work because it isn't profitable for them to staff an associate who's billing 800 bucks an hour on a task like that. And it's a bit of a prisoner's dilemma in that if any of your

competitors do something, you have to adjust to keep up with the pace of play. So even if you're theoretically have the mindset that you're talking about, if someone else does it, then you're certainly suddenly inefficient or you're billing for things that other people won't. That's exactly it. You have a almost perfect equilibrium of the service. And as soon as somebody moves down, it forces everybody to do it because there's very low differentiation on particular tasks like that.

I think we want to dive into a little bit of the AI within Legora. But I guess one question that just at like a business level is there's other players, unfortunately, in the space that you're operating in. I'm curious what advantages you feel like you've had both coming from the

the Nordics and also maybe starting six months, 12 months after some of the other players in the space. What does that advantage you as a company? So I think starting in the Nordics, it's almost like we were this really, really small fish and we got to eat

eat our way to become a bit of a bigger fish. Then we suddenly became a crocodile in this smaller pond. Now we're jumping across the Atlantic. We're coming to the States. We just opened in New York. Frankly, the great thing was Europe is such a fragmented market in every country, effectively.

Our market, our initial market was very small. And that was one of the first things that we got pushed on during Y Combinator. It's like, how are you going to move from Sweden? And I said, well, we're not. We're going to do Sweden. We're going to win it and then we'll move. And funny enough, I think one of the main advantages being

the sort of fast second mover if you will is you can observe you know what's working what's not working I think initially there was a lot of focus on we need to train our own llms we need to be you know an AI company first that's like pushing the research in this field and you know coming second with also significantly less funding I mean our initial Angel round was 50 000 and we just said hey

we don't have the money to do that. We don't have the time. We don't have the energy. Let's just build an application and let's focus on the application layer and build something that at the end of the day, people are really excited to use. And coming from sort of a non-legal background, it also forces you to be very humble and be very, uh,

attentive to what everybody's saying. So, what are the clients saying? How is the relationship between a law firm and their clients developing? And I think this is also now pushing us ahead where we can move from being just an internal facing tool into really focusing on the entire relationship that exists in the delivery of high quality legal work. I did a podcast one time with Daniel Leck and he said starting in the Nordics allowed him to, had he started in the US, they would have

made him skinny down his product a little bit into a more narrow functionality. But because he started in this other pond, he could be a little bit more ambitious unknowingly because he serviced the totality of the needs of the market. I'm curious if that's something you felt...

I don't think I've thought about it that way, but I think if you look back on it, it kind of explains what we did, right? There's a lot of legal tech companies that are solving a very niche problem. Drafting. Drafting, contract review, or they solve search. It might be super, super narrow. And even if you look at a big law firm, you've got litigation, you've got corporate, you've got the transactional team, and they work very differently. And we sort of said, hey, we want to service...

every lawyer. I want every lawyer who's serious about doing great work and making money to use Legora, just as every great designer uses Figma. And perhaps you wouldn't be that ambitious had you started here because of the sort of competitive pressure, right? I think there's also one nice thing about being able to serve enterprise in your local market to the point where you're already enterprise ready when you enter a new market.

And law firms are very well connected. If you're a large firm in Sweden, you have a almost partner firm or a good friend firm in France and in Germany and in Spain and in the US. So by starting to work with a couple of firms, you also get really great referrals because they all, you know,

want to lean in and kind of do it the same way. I mean, I think one thing that's really powerful about your product, as you just alluded to, is just, you know, how broad the capabilities are and you really are able to serve kind of end-to-end at these law firms. I know a lot of builders out there are kind of grappling with some problems, you know, or thinking through what happens when I have this end-to-end product? How do I, like, teach people to use it or

it or how to get started with it. And I'm wondering what you've learned now with all these deployments you've done about how to teach lawyers to use these tools. Well, I think the short learning is it takes way more work than we thought it would. One great example is Jem, who comes from Baker McKenzie. He was responsible for the Gen AI rollout, and

It was funny talking to him because in the initial traditional software rollouts he did, you would be thrilled to get 5-10% adoption. That would be a great number. But for any other enterprise software, that is awful. Those metrics suck. And now with Legora, when he's doing other client deployments for us...

We're increasingly hitting numbers like 70-80% adoption and it's a different world when the lawyers are actively approaching the innovation team saying, "Hey, we want access. We want these tools." And that hasn't really happened in this industry before.

How do you think about where to build towards with the models constantly improving? You want to skate to where the puck is going in some ways, but you also have to be pragmatic about what's actually applicable today. And so how do you think about that balance? And at what point you sort of think about product roadmap meeting model development?

You can think about the models, but I also think it's useful to think about these AI labs as platforms and software companies. Because frankly, OpenAI, Cloud, Gemini, they're for sure model providers, but they're also increasingly product companies. Anthropic is building out Cloud to connect with other systems. Google is building out Gemini to sort of sit on top of the entire Google workspace stack. And

They're not only pushing innovation on the models themselves, but also on the way that they interact with other pieces of the software and system. So the way that I've always thought about it is if it's something that the AI labs are going to build and at some point make available to builders like us, then we should not build it. That's sort of going totally against the stream. And you want to build everything like boats so that when the tide rises, all the functionality just gets better. But a great example would be

We just released a new feature called Playbooks, where you give Legora a set of rules and typical fallbacks and points of how it's supposed to negotiate. Let's say you outline 20 different rules for how to negotiate an NDA or an MSA.

Now, Liguor needs to go through them one by one by one to make really high quality edits. But if the models become so good that you just say, "Hey, here's a playbook in a Word document or in an Excel file, take this under NDA and cross-reference them and give me all the red lines," then you don't really need to build the feature that way. So that's something where we think it adds a lot of value building it that way today.

five years from now, maybe that's completely unnecessary. Yeah, it's such an interesting tension where it feels like you want to provide value to your customers today with whatever the scaffolding you need around the models, while also realizing that some of that scaffolding may just be completely irrelevant in two, three years. Exactly. And I think another great example would be workflows, like multi-step instructions. Typically, you would have these sort of node-based, no-code builders where you have the output of one block serving as the input for the second block,

And you need these kind of technical builders to come in and set up these workflows. But the LLMs themselves with proper planning and then execution with tool use are fully capable of on the fly creating their own workflow or like their own plan of it. And so the only thing you need to do is really provide it with an instruction and say, hey, I want you to do this task. And then it's going to be able to figure it out themselves given the tools that are at its disposal.

I realize there's no perfect way of predicting what the model capabilities are going to be even tomorrow, let alone six, 12 months from now. If you can, let us know. Yeah, that would make our job a lot easier if you figure that out. But I guess at a practical level, that point that you made about what exactly can be done potentially in the future versus how you need to do it today, do you have some framework that like, hey, if it really feels like this could be...

done by an LLM in the next three months or six months, we might let's hold off, let's table it versus it feels way in the future, let's just go build it. Like, how do you think about that? You just saw Claude and Anthropic release citations as part of their API. Now, you know, we built citations really, really early on because it's like critical for our use case and for lawyers to be able to reference and see, okay, the LLM made this response and its references to this material and this specific text chunk. But

you know, if the LLM providers give that for free in half a year, then we'll just deprecate our entire code. I mean, my way of thinking about it is if somebody else is doing it, then let's use that. What's like the hardest part of building these products today? I think the hardest part is kind of to what

Logan was alluding to, there's a hundred different things that you kind of want to be doing and they're all really high value and then you need to prioritize like which five are we going to go after and then how do these five tie into each other in a very cohesive platform because it's easy to build a Frankenstein monster if you just always go after like the next hot thing and

I think some other companies in our space have made that mistake of just building stuff that kind of makes sense but the totality and the sum of it isn't really well thought through.

So it's hard to plan for how your platform is going to look like and be structured just from even like a database perspective where, okay, we have an organization and then we have projects and these projects can maybe be shared with a client. Like how is all of this going to look like and how do we build the functionality so that that fits the paradigm for what we're going for?

Here, as well, I think the law firms themselves and the in-house teams are figuring out what they want as well. We're all trying to figure out exactly the best way to apply the technology, and then we need to align on a future that makes everybody win. Some people put the cost structure in some ways on the client that you map, "Hey, this is what it costs us, and therefore, this is how we're going to bill you."

That's not how you guys bill today. I'm curious how you think about the pricing and the value and the cost and how those three things kind of all interact and therefore what that means as model costs continue to decrease. How do you pass that on to the customer? Yeah, this is a great question. I was talking with Varun from Clay about it in a cab the other day. And we have the problem of...

running a seed-based model, which I think is easy to buy. It's easy to predict. You know what you get. But then just the other week, we had a user rack up 10,000 bucks in LLM costs. So I think over time, it's not hard for me to imagine where you'd have kind of a platform fee and some usage element to that. But it also depends on how the LLM providers themselves develop their pricing. I think the early...

thoughts around all of this was LLM prices will continue to go down. And so if LLM prices continue to go down, we're willing to take a bit of a hit in the beginning to get positive and better margins over time. Now that's not really happening because the LLM models are becoming so much better, but also more expensive. I don't know if you've tried O3, but O3 is incredibly good at a lot of legal work.

It's incredibly, incredibly good, but it's also very, very expensive. And so I do think that there's kind of an element to this where it's like, do you need the...

the bazooka or do you just need a handgun for like the task that we're doing? And increasingly so, we're running classification algorithms and model pickers that kind of pick the best model for the task. I think it's such a fascinating point because it's probably, you know, next year, that same kind of recreation might actually be quite cheap, but it'll also be so last year and there'll be some other model capability that's even more powerful that folks will end up wanting.

Exactly. What about on the infrastructure side? You're obviously one of the biggest users of LLMs out there today. How do you think about some of the gaps in infrastructure tooling that exists, some of the stuff you've used that you found helpful? The most interesting thing to me is MCP, where we can give Legora access to call on outside tools. We can provide a set of tools that are already good and

let's say we wanted, we give Legora access to Redline and Documents, right? And then that's a tool that the LLM can use. But the really interesting part becomes when our clients can provide tools as well. So you'll have Legora access a client-specific CRM or a client-specific knowledge database or a set of templates or it's able to push notifications to the client's emails or something like this, right? And that sort of

very, very quickly expands the universe for what's possible. Since we're a venture-sponsored podcast, I have to ask you every VC's favorite question, which is, how do you think about what the moats are in AI applications? The moats, of course, look very differently across industries.

take Figma. I think the moat in Figma is that it's kind of the system of record and it's the platform where both the designers, PMs, marketers are collaborating, but also where they're directly collaborating with their clients. And I think for us, there's a lot of interesting...

directions to explore there. And, you know, we currently integrate on top of a lot of our customers' own data. We integrate with outside databases for things like case law, legislation, up-and-coming regulation. And a lot of it actually sits in the

system that you'd like to work in, I think. It's sort of a taste-based, "I like working with this thing." I think if you grab the average lawyer in big law, they spend 80% of their time in Microsoft Word, Outlook, and iManage, their document management system. And I think that's about to change.

Yeah, no, it's funny. This is one we love to debate internally. And I feel like it's always the question of like, is this just literally the same stuff as SaaS? And I feel like in many ways, in the early days of AI apps, you know, as you mentioned earlier, people were training their own models, they were doing all these things that felt like, oh, that's going to be super differentiating. And then quietly, I feel like all that's gone away and people have realized...

not as relevant. No, for sure. And I mean, look, there were a lot of companies in our space too that said that the moat is within fine-tuning models. And we were very clear from day one and said, there's no moat at all there. And we're going to work with every model to build the best system. How do you think about building the system and the architecture that gives you the ability to decide that

this model here, this model there and make sure it's flexible and future proof. So I feel like I'm just doing advertisement for a lot of the tools that we use internally. But, you know, one of them is brain trust, where we've set up thousands of evals and it's extremely easy to run an evaluation on a new model as soon as it becomes available. And very importantly for our clients is that every model we use goes through security, privacy, you know, legal review so that

It conforms with our data processing agreements because when you work with law firms, you've got to be on the right side of your contracts. The way that we've structured a lot of it is, let's say you have multi-step workflows. We then have evals for each step, and then we have evals for the complete end-to-end product. And we test a lot of the permutations around using the different models in different places. And those really high-quality reasoning models are incredibly good.

they're just very hard to price when a new model comes out do you have like a go-to thing you test or or put another way is there like some capability you can't wait like some prompt you're like i'm so excited for the day that this thing works wow that's a good question and the

The sort of most complex thing that you might look to solve in a one-off prompt would be drafting a really long and complicated document. And the reason why that's hard is a comma there or a word there can directly influence the meaning quite a lot. And many of these contracts are not...

open source or not available on the web. So the LLMs don't necessarily get trained on them, right? Like, yeah, you have stuff on SEC and Edgar, but a lot of the, you know, share purchase agreements that, you know, end up sort of being the driving precedent, they're firm specific. And so,

the models aren't necessarily getting trained on how to draft really high complex contracts like that. But then again, we can give a model or an agent access to a tool to access a repository to use that as the basis for a draft that it does. I guess shifting gears to, you know, uh, Lagora the company, um, I'm curious, like obviously you've had, you know, kind of a fascinating journey and so maybe to hit some different parts of it, you know, you obviously were in YC in this like post-chat GPT craze, like,

What was that like? Now I'd say it feels like every ambitious founder is building an ad company. Did that feel in the air at the time? Or what was it like being in some of those batches right as this craze was starting? So we were, I think, one of the first acceptance into our batch. So we got accepted in August for the winter batch. So it was like an early... We got in early. And as soon as we got in...

I think this is very unconventional and maybe not something I recommend, but I took out a loan against our investment coming in from YC because we had a Swedish corp and had to flip it to a Delaware. And that was going to take like three months. And we needed the money now just to hire like four more engineers to run at this. So I think the feeling very much was,

The world is moving really fast and we need to do so as well. And when we got to YC, of course everybody was working with AI, it felt like. They were either building a vector database or a Postgres layer on top of it, or they were building an application layer.

I didn't take part in YC that much because I was doing all of our commercials at the time. So I bought this ring light to put on my laptop, like a thing that influencers have. And I was up between 1 a.m. and 10 a.m. for five, six days straight just doing sales to Europe. And then I kind of crashed and I said, fuck this, I'm going back to Stockholm. And the ring light, to be clear, was so it didn't look like it was the middle of the night. Exactly, yeah, yeah.

But then I would sit in the kitchen. I think we have some really fun photos of me and Tuhin, our first growth hire, sitting there and just crunching. And frankly, I think a lot of the YC companies, they build a lot. They're just very, very focused on building. We were very, very commercially focused from the beginning. And I think we really took the advice to heart that launch as soon as you have something. And to be frank, the first thing we had was

basically a private and compliant chat GPT with better rag on their own documents and Swedish legislation. But that was good enough for that time. And then, you know, every single week the bar increases.

for where you need to be to be best in class. I'm curious, because this idea of launches as soon as you have something, I mean, I feel like in this AI wave, we're seeing people make really big promises ahead of where products are and selling maybe the furthest ahead that we've seen in quite some time. And I think one thing, you've been very methodical about when it made sense to release product. I remember right after our investment, you were like, we want to do more work on this product before we bring it into other markets. How have you thought about that transition

tension of, you know, God, this market's moving so fast to, hey, I get one first- Five million. And, you know, look these guys in the eyes and say, yeah, we're not going to sell for the next, you know, four or five months. We're like, great.

Yeah, I got some looks from Logan. It was a very mature decision, albeit as a first board meeting. It's the first time I've ever had that happen in my venture career. It's worked out. And I think to your question earlier, Jacob, you get one chance.

You get one chance with, especially with, I don't know if this is especially with lawyers, but I say developers, if you're building, you know, vibe coding software, they're easier to approach once and then say, oh, we have some new updates. Do you want to come and try again? Because they like that idea of,

You're starting somewhere and you're building and you get to come along the journey. Now for us, if an attorney comes in and they do a query and it doesn't work out, then we see them fall off. They don't come back. And so reactivating them is really hard.

I mean, in the beginning, there were a lot of just reliability, infrastructure, just the way that you do chunking and you get the RAG system to deliver at a certain expectation and then just being able to serve like thousands of users on the platform a day. Those were problems you had to solve in the beginning. And if you don't solve them and you try to onboard a firm like Cleary Gottlieb,

it's not going to go so well. And as we've moved from serving large firms in Europe into now serving some of the largest firms in the world, the expectations are incredibly high. And we're also starting to see that we have become a system of record and a system that is more and more frequently being relied on for the end-to-end deliverable.

If something isn't working, now we get immediate phone calls, emails, Slack messages like, "Hey, we need this thing up and running because we have a client deliverable to get to here." So I think, yeah, that was a mature choice. But now that we're a bit bigger, we're not doing this big spring release and winter release yet because frankly,

Everybody wants more all the time, but we've moved from ship it as soon as we have something into let's work with a couple of design partners, get it to the point where they're thrilled about it, and then do a proper launch. I'm curious, the pace that you move at is seemingly the pace that AI moves at, which is...

I don't know, a hundred miles an hour and just all over the place, things changing all the time. I call you, you're in one city, then the next day you're in the next city. And then the next day you're in the next city. And all those are three different continents that you're crossing or something. Uh,

But I'm curious how you're able to get that to manifest within the business itself. You have this innate drive and hunger to do all that. How does that trickle down into the business? How do you make sure that that manifests itself in the go-to-market team, the product team, the engineering team, and all those things? So I think in Stockholm, because we have engineering and product based there, and then we have three commercial hubs with New York, London, and Stockholm as well. We...

You had Klarna, you had Spotify, you've had a couple of larger companies come out of there. And then it's been quite quiet for a long time. There hasn't been that much action. Web3 never really took off. And then you had AI. And I think you had a lot of people in the city that want to work really hard for a mission and a product and building a company that they believe in. And so for us, we've been able to attract, I think,

you know the players with the most urgency to like really get done and that also comes from i think my co-founder sigge like i've i've never seen somebody just sit and code as much as he does it's like 14 hours a day he's in the office coding and then he does one hour of climbing in the morning to stay sane you know seven days a week and i think if if you continue to run at that pace and you know the whole company sees that it trickles down and

We've also been very upfront with that. So when we recruit, we're very upfront with this is not a nine to five job. We're not here to maintain something. We're here to build for the future. And if you're excited about that, hop on board. If not, I'm sure there's another great company that you can go and work at.

I think you set that expectation with us pretty clearly. I remember, I think the first time we ever chatted, you were in Estonia at a conference. It was like 11 p.m. and you were drinking a Red Bull. Yeah. I feel like that's, if we were to introduce you in a novel, that's how I feel like your character would be introduced. Yeah. And it was fun when we signed the term sheet with Iconic. I think Seth was over at the office at like 1.30 a.m.,

And Manasiga was still there working. We had a conversation in December where it's like, hey, we have one of two paths. We can go down. We can just fortify the flank and dominate Europe. Or we can go to the U.S. And you almost cut me off before I finished the question and said, no, there's no question. We're going to go to the U.S. I guess, what have you found in shifting from a European perspective?

business and largely based in Stockholm, a little bit in London, which gosh, I mean, six months ago we were, which is amazing where we are today, to moving at hyperspeed, coming into the US and being able to balance the culture and try to make all that stuff work together. So we force everybody to do like a week or two weeks of onboarding in Stockholm. And we've been very

diligent on no remote work. We're fully in office. You know, it's like momentum breeds momentum, you know, people love winning. And I think we've been able to recruit people who love winning and who hate losing. And so the losses feel really hard. And frankly, for the past three or four months, we haven't had that many losses, which is nice. But in the beginning, I think every

you know every customer in the beginning that came back with negative feedback or when we lost a deal because we weren't seen as like big enough or a big enough name that really hurt and now it's felt great to go back and even having them you know reach out to us saying hey we just saw you raised your series b can we please have another conversation you know i think we've we've very quickly moved from being a small swedish-based startup like a year ago we were 10 people now we're 100.

And recruiting 90 people in a year when you're that few and getting it right, that's hard. I think this part is so interesting. I mean, I feel like there was kind of a typical growth rate of a company that we all got used to in the SaaS era. And it's like, all right, you build for a while, then maybe you'd get a big corporate logo, and then maybe you get a few more. And it feels like you're probably at the vanguard, but there's a lot of AI companies that are having this kind of growth rate and employees and getting to these insane customers and pretty short order customers.

I'm sure you talked to a lot of other founders, like, you know, maybe from the previous generation. To what extent, like, does, you know, is company building feel different in like this kind of pace? And to what extent, you know, you know, we talk about sometimes we're like, oh, an AI series C company can feel like a series A company at its bones. Like, how is that, you know, maybe reflect on that. Yeah, I think you're completely right in that.

there's a new expectation for how quickly a software company can grow and i think a big part of that is you're not going in to replace an existing piece of software you're creating a completely new category right that like that that is different and you've had

times like this in the past during the internet and during mobile where you know things were just possible that you couldn't build before and so whoever builds the fastest has the highest velocity the best product the best service they're going to end up dominating the market and

The really cool thing for us was our first client, Mannheimer-Svartling, and our first design partner, they were an enterprise client. They're a huge company. They're the biggest law firm in the Nordics, and they take really seriously on the work that they deliver.

by sort of osmosis, I think that colors off and you're not on this journey where you start serving other startups and then you start serving SMBs and then you dip your toes in enterprise. We were like, day one, enterprise. And we spent, I don't know if I told you this, we spent half of our initial angel funding on SOC and ISO certifications.

And I didn't take a salary for the first six months so that we can actually afford doing that. Well, we always like to end our interviews with a standard set of quickfire questions where we squeeze in a bunch of overly broad questions right at the end. To start, what's one thing you think's overhyped and one thing you think's underhyped in the AI world today?

I think MCP is both underhyped and overhyped. I think it's underhyped in the sense that it's fundamentally making it possible to serve kind of a universal app into a lot of other different capabilities, but it's also completely overhyped in the sense that everybody's talking about it and trying to just POC it, and it's...

It's still not yet moved its way into full production. You kind of need to get some things right around authentication and other things. What's been the biggest surprise in building AI features at Legor? Maybe one thing that you didn't think would work that actually worked quite well or one thing you were so confident would work and didn't quite take. The most surprising thing is actually how people start using it. When we dropped our drafting thing, the first thing that happened was an attorney comes in and they just give a query, which is, write me an SPA.

And I'm like, of course that's not going to work. Like, what are you expecting here? So I think the expectation management is like surprising how it's, yeah. How do you teach people how to use the traffic? Exactly, exactly, exactly. But, you know, that's why we do all these trainings. That's why we do the onboardings. That's why we do, you know, office meetings with all these big firms that we kind of go around to. And, yeah.

It has surprised me that people come in with so different expectations. You can have associates who are super savvy and they know exactly how they're going to set up the workflow, they have their templates, their prompt library, and they're just so on it. And then you have folks coming in and they expect the world.

Yeah. That's hard. Well, I think one of these things that's interesting probably is, you know, as these model capabilities change every six months, you probably have to go back and be like, well, actually, now maybe you can write that query. Yes. Yes. I've thought a lot about how we can productize the onboarding of these things. What's something you changed your mind on since you started Logora? Our first, the first version of the product, you've never seen this, that was summer 2023. It was completely button and workflow based. So you would have basically a library of things

nine different buttons in different colors and one of them would be called summarize the other one would be called check document against policy and another one would be called search database so you had to go and click them and then you'd follow a very structured flow

One of my co-founders was very in love with that idea, and I said, "No, let's do the chat." Because I thought the chat would give us a better interface to do more, and then we could always add these things as functions or tools or things that the chat can then use.

Moving from that to the other world, it basically meant that we deleted 95% of our source code as soon as we got accepted into YC. We've taken a lot of contrarian moves. I think that was also one of the good pivots we made. If you weren't in building a legal space and you were just getting started on something new now, what other ideas in AI get you excited? I'm so in the legal world that it's hard to think outside of it.

But just some of the things that I'm passionate about outside of this. I think CROs in pharma has like the biggest... It's one of the biggest disruption opportunities in the world because it's so manual. There's so much data. One of my family members works with clinical trials. They pay billions, billions to these CROs, which are effectively like slow-moving...

slow moving consultancies that have frankly pretty structured workflows. So I think the first fully AI CRO that can ensure and deliver like end-to-end deliverables will, yeah.

they will do really well. I like that. Obviously, a lot of Agora is text-based today. Any multimodal use cases to get you excited? Video, audio, image? One of the things I'm most excited about is just starting to work with voice and audio transcripts, both in the sense that you can instruct Agora in voice, but also use things like transcripts, audio files, because if you look at a typical deposition, that's being recorded, and now

Now, you might previously have to hire somebody to sit down and note that down, which then serves as the input for wherever you take the work. But now, just uploading the audio files, transcribing them, and starting to work with them as documents and interrogating them is, I think, a great use case. Obviously, your tool can do a lot of legal work. As folks think about going into a career in law, what skills will matter for a lawyer in the future? If you're in law school right now, for those listening, what should they focus on? So,

I've spoken with a lot of managing partners and management groups on this because they're thinking really hard about how they're going to upskill and train their new associates and the lawyer of the future. As a side note, one of the funniest usage

peaks that we've had was when a Spanish firm had their whole new cohort come in and they all became like Legora fans the day one because it was part of the onboarding and then the usage for that firm just like spiked and

But previously you might have hired underconfident overachievers that are really good at following instructions. They're very thorough and they sort of do things step by step by step. I think now you're going to need entrepreneurial, creative people

people who maybe challenge the existing ways things have been done because you will have partners who are sitting on top of their pyramid kind of farming and doing work there but

a lot of the processes within six to 12 months, I think, will be not disrupted, but augmented with AI elements. And I think you do want people who were upskilled or who were very fluent in working with AI during their legal studies. Yeah. I mean, it's really interesting. It's like everyone's going to be a manager from day one of a bunch of AI agents. And so it actually is a very different skill set than being

a diligent associate. Completely. You could say, "Hey, we think it's the innovation departments or the IT department's job to do that, but I think it's frankly up to every individual to kind of augment themselves with this." I think that's starting to become very clear in the sense that you see all this again, like Twitter and LinkedIn pieces coming up from tech CEOs, like, "Hey, you need to augment your own work with AI and show us how you're doing that." I think we'll have expectations like that in law firms as well. Yeah.

Well, Max, this has been a fascinating conversation. I'm sure folks will want to go learn more about Legora, the work you're doing. The mic is yours. Where can folks go to learn more? So since we changed the name from Leia, we've actually bought the .com. So you can find us at legora.com. And I think the future is just too exciting to be left to just software engineers. And we really want to collaborate with law firms and legal professionals in building this future. So please reach out and I would love to chat. Amazing. Well, Max, thanks so much for coming in. Awesome. Thanks for having me, guys.

so